Department of CSE (Artificial Intelligence and Machine Learning) of ACE Engineering College, India.
International Journal of Science and Research Archive, 2025, 14(01), 1208-1215
Article DOI: 10.30574/ijsra.2025.14.1.0199
Received on 11 December 2024; revised on 18 January 2025; accepted on 21 January 2025
Source code plagiarism detection has become a critical area of research with the increasing prevalence of code reuse in academic and professional settings. In order to achieve thorough code comparison, this work introduces a novel tool for detecting source code plagiarism that combines lexical similarity, abstract syntax trees (ASTs) and cosine similarity. The system incorporates a dynamic front-end that was created using Streamlit, providing an intuitive user interface with a code editor that can run code. Through the "Check Similarity" feature, which calculates the plagiarism percentage and finds the most similar file, the application offers real-time plagiarism detection. The methods, benefits, and difficulties of various approaches are examined in this study, with a focus on how well they identify structural and syntactic similarities. The suggested system has a great deal of promise for academic and professional environments, offering reliable and efficient plagiarism detection.
Source Code Plagiarism Detection; Cosine Similarity; Abstract Syntax Trees (AST); Lexical Similarity; Streamlit Framework; Scikit-Learn Module
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Pradeep T, Adithya Kumar Gonepally, Arun Kumar Pusala and Charan Teja Singarapu. Anti-plagiarism tool helping developers to generate authentic code. International Journal of Science and Research Archive, 2025, 14(01), 1208-1215. Article DOI: https://doi.org/10.30574/ijsra.2025.14.1.0199.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0







